@InProceedings{zhu-tian-kbler:2019:S19-2,
  author    = {Zhu, Jian  and  Tian, Zuoyu  and  Kübler, Sandra},
  title     = {UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {788--795},
  abstract  = {This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6: Offens-Eval. We take a mixed approach to identify and categorize hate speech in social media. In subtask A, we fine-tuned a BERT based classifier to detect abusive content in tweets, achieving a macro F1 score of 0.8136 on the test data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C, we used a linear SVM with selected character n-gram features. For subtask C, our system could identify the target of abuse with a macro F1 score of 0.5243, ranking it 27th out of 65 submissions.},
  url       = {http://www.aclweb.org/anthology/S19-2138}
}